Something Borrowed: Exploring the Influence of AI-Generated Explanation Text on the Composition of Human Explanations
Why this work is in the frame
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Bibliographic record
Abstract
Recent advances in Human-AI interaction have highlighted the possibility of employing AI in collaborative decision-making contexts, particularly in cases where the decision is subjective, without one ground truth. In these contexts, researchers argue that AI could be used not just to provide a final decision recommendation, but to surface new perspectives, rationales, and insights. In this late-breaking work, we describe the initial findings from an empirical study investigating how complementary AI input influences humans’ rationale in ambiguous decision-making. We use subtle sexism as an example of this context, and GPT-3 to create explanation-like text. We find that participants change the language, level of detail, and even the argumentative stance of their explanations after seeing the AI explanation text. They often borrow language directly from this complementary text. We discuss the implications for collaborative decision-making and the next steps in this research agenda.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it